I am a postdoctoral fellow in the lab of Arnold Kriegstein at the University of California, San Francisco (UCSF). I focus on applying single-cell genomics techniques to study the development of specific cell types of the human brain, as well as to understand how these cell types are affected in various diseases, especially autism. Before starting my work at UCSF, I did my PhD at the University of Miami focusing on genomic analysis of autism. I did my B.S. and MS at Moscow State University in my native Russia, where I worked on animal models of epilepsy and Alzheimer’s disease.
Dmitry Velmeshev
Postdoctoral Scholar
University of California, San Francisco
From this contributor
Single-cell analysis suggests brain signaling problems in autism
Recent advances in technology allow researchers to measure RNA that is contained within the nucleus of a single brain cell.
Single-cell analysis suggests brain signaling problems in autism
By
Dmitry Velmeshev, The Conversation
22 May 2019 | 5 min read
Explore more from The Transmitter
Links between tuberous sclerosis complex and autism, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 17 August.
Links between tuberous sclerosis complex and autism, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 17 August.
By
Jill Adams
18 August 2026 | 1 min read
The fun and flexibility of data science
Taylor Bolt spent his Ph.D. and postdoc digging through brain imaging data for clues to cognition. In industry, the datasets are different but the joy of answering questions with data remains.
The fun and flexibility of data science
Taylor Bolt spent his Ph.D. and postdoc digging through brain imaging data for clues to cognition. In industry, the datasets are different but the joy of answering questions with data remains.
By
Katie Moisse
18 August 2026 | 7 min read
Mind over metrics: How can we tell if two brains (or AI models) are alike?
The ability to record from large populations of neurons has triggered the development of myriad methods for comparing them. But we’re still grappling with how to convert measures of likeness into a better mechanistic understanding.
Mind over metrics: How can we tell if two brains (or AI models) are alike?
The ability to record from large populations of neurons has triggered the development of myriad methods for comparing them. But we’re still grappling with how to convert measures of likeness into a better mechanistic understanding.
By
Alex Williams
17 August 2026 | 7 min read